Optimize time-series data in hypertables

Hypertable are designed for real-time analytics, they are Postgres tables that automatically partition your data by time. Typically, you partition hypertable on columns that hold time values. Best practice is to use timestamptz column type. However, you can also partition on date, integer, timestamp and UUIDv7 types.

To follow the steps on this page:

• Create a target Tiger Cloud service with the Real-time analytics capability.

  You need your connection details. This procedure also works for self-hosted TimescaleDB.

Create a hypertable for your time-series data using CREATE TABLE. For efficient queries, remember to segmentby the column you will use most often to filter your data:

CREATE TABLE conditions (
   time        TIMESTAMPTZ       NOT NULL,
   location    TEXT              NOT NULL,
   device      TEXT              NOT NULL,
   temperature DOUBLE PRECISION  NULL,
   humidity    DOUBLE PRECISION  NULL
) WITH (
   tsdb.hypertable,
   tsdb.segmentby = 'device',
   tsdb.orderby = 'time DESC'
);

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When you create a hypertable using CREATE TABLE ... WITH ..., the default partitioning column is automatically the first column with a timestamp data type. Also, TimescaleDB creates a columnstore policy that automatically converts your data to the columnstore, after an interval equal to the value of the chunk_interval, defined through compress_after in the policy. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads while also saving significant storage space. In the columnstore conversion, hypertable chunks are compressed by up to 98%, and organized for efficient, large-scale queries.

You can customize this policy later using alter_job. However, to change after or created_before, the compression settings, or the hypertable the policy is acting on, you must remove the columnstore policy and add a new one.

You can also manually convert chunks in a hypertable to the columnstore.

To convert an existing table with data in it, call create_hypertable on that table with migrate_data to true. However, if you have a lot of data, this may take a long time.

When you set timescaledb.enable_direct_compress_copy your data gets compressed in memory during ingestion with COPY statements. By writing the compressed batches immediately in the columnstore, the IO footprint is significantly lower. Also, the columnstore policy you set is less important, INSERT already produces compressed chunks.

Note

Please note that this feature is a tech preview and not production-ready. Using this feature could lead to regressed query performance and/or storage ratio, if the ingested batches are not correctly ordered or are of too high cardinality.

To enable in-memory data compression during ingestion:

SET timescaledb.enable_direct_compress_copy=on;

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Important facts

• High cardinality use cases do not produce good batches and lead to degreaded query performance.
• The columnstore is optimized to store 1000 records per batch, which is the optimal format for ingestion per segment by.
• WAL records are written for the compressed batches rather than the individual tuples.
• Currently only COPY is support, INSERT will eventually follow.
• Best results are achieved for batch ingestion with 1000 records or more, upper boundary is 10.000 records.
• Continous Aggregates are not supported at the moment.

You can alter a hypertable, for example to add a column, by using the Postgres ALTER TABLE command. Some operations are not supported for hypertable with columnstore enabled. See Altering hypertables with columnstore enabled.

You add a column to a hypertable using the ALTER TABLE command. In this example, the hypertable is named conditions and the new column is named humidity:

ALTER TABLE conditions
  ADD COLUMN humidity DOUBLE PRECISION NULL;

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If the column you are adding has the default value set to NULL, or has no default value, then adding a column is relatively fast. If you set the default to a non-null value, it takes longer, because it needs to fill in this value for all existing rows of all existing chunks.

You can change the name of a hypertable using the ALTER TABLE command. In this example, the hypertable is called conditions, and is being changed to the new name, weather:

ALTER TABLE conditions
  RENAME TO weather;

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You can change the data type of a column in a hypertable using the ALTER TABLE command. In this example, the temperature column data type is changed from DOUBLE PRECISION to NUMERIC:

ALTER TABLE conditions
  ALTER COLUMN temperature TYPE NUMERIC;

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The following restrictions apply:

• You cannot change the type of segmentby columns.
• For time dimension columns, you can only change to TIMESTAMPTZ, TIMESTAMP, DATE, INTEGER (smallint, integer, or bigint), or UUID (UUIDv7 only).
• You cannot change the type of columns with custom partitioning functions.
• You cannot change the type of columns for hypertables with columnstore enabled. See Altering hypertables with columnstore enabled for how to do it instead.
• For columns with statistics enabled, you can only change to integer or timestamp types. To change to other types, first disable statistics using disable_column_stats.

You can drop a column from a hypertable using the ALTER TABLE command. In this example, the humidity column is dropped from the conditions hypertable:

ALTER TABLE conditions
  DROP COLUMN humidity;

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You cannot drop partitioning columns.

Drop a hypertable using a standard Postgres DROP TABLE command:

DROP TABLE weather;

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All data chunks belonging to the hypertable are deleted.